Ultrasonic diagnostic apparatus, ultrasonic image processing apparatus, and ultrasonic image processing method

ABSTRACT

Multiresolution decomposition of image data before scan conversion processing is hierarchically performed, low-frequency decomposed image data and high-frequency decomposed image data with first to n-th levels are acquired, nonlinear anisotropic diffusion filtering is performed on output data from a next lower layer or the low-frequency decomposed image data in a lowest layer, and filtering for generating edge information on a signal for every layer is performed from the output data from the next lower layer or the low-frequency decomposed image data in the lowest layer. In addition, on the basis of the edge information on each layer, a signal level of the high-frequency decomposed image data is controlled for every layer and multiresolution mixing of the output data of the nonlinear anisotropic diffusion filter and the output data of the high-frequency level control, which are obtained in each layer, are hierarchically performed.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is based upon and claims the benefit of priority fromprior Japanese Patent Application No. 2007-338276, filed Dec. 27, 2007,the entire contents of which are incorporated herein by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to an ultrasonic diagnostic apparatus, anultrasonic image processing apparatus, and an ultrasonic imageprocessing method of transmitting an ultrasonic wave to the inside of atested body and obtaining diagnostic information inside the tested bodyon the basis of a reflected wave from the inside of the tested body. Inparticular, the present invention relates to removing of a speckleincluded in image data.

2. Description of the Related Art

The ultrasonic diagnosis makes it possible that the pulsation of theheart or the movement of an embryo is displayed in real time by a simpleoperation of bringing an ultrasonic probe into contact with a bodysurface. In addition, since the ultrasonic diagnosis is very safe, thetest may be repeatedly performed. In addition, the system size is smallcompared with other diagnostic apparatuses, such as an X ray, a CT, andan MRI, and a test at the bedside can also be easily performed. For thisreason, it can be said that the ultrasonic diagnosis is an easydiagnostic method. An ultrasonic diagnostic apparatus used in theultrasonic diagnosis changes in various ways with the type of a functionthat the ultrasonic diagnostic apparatus has. As a small ultrasonicdiagnostic apparatus, an ultrasonic diagnostic apparatus that is sosmall as to be carried with one hand is being developed. In addition,since the ultrasonic diagnosis does not cause radioactive exposureunlike the X ray, the ultrasonic diagnosis may also be used in anobstetric treatment, a remote medical treatment, and the like. Inaddition, a recent ultrasonic diagnostic apparatus may collectthree-dimensional biological information (volume data) by spatiallyscanning the inside of the tested body using an ultrasonic probe with atwo-dimensional array in which ultrasonic vibrators are arrayed in atwo-dimensional manner.

However, received signals from a plurality of adjacent tissues of atested body interfere with each other due to a phase difference thereof.In addition, an image pattern differently viewed, from that in the caseof mixing only amplitude information, that is, a speckle is generated.Since this speckle interferes with correctly observing the position andshape of a boundary of tissues of the tested body, various kinds ofprocessing methods for removing the speckle have been proposed.

As one of the methods, there is a method of performing multiresolutiondecomposition of an object image by wavelet transform/inverse wavelettransform and performing processing for applying a threshold value,weighting, and the like to a high-frequency component of an imagedecomposed at each level. Although a speckle is removed in this method,there is a problem that an obtained image depends on artificialsensibility.

Accordingly, for example, JP-A-2006-116307 proposes a method ofdetecting an edge of an image decomposed at each level, calculating thedirection of the edge for every pixel, and performing filtering formaking the edge smooth in the tangential direction and the edge clear inthe normal direction. Also in this case, however, there is a limitationin performance because making the edge smooth and clear is performed bya fixed filter.

On the other hand, a method of removing a speckle by a nonlinearanisotropic diffusion filter has also been proposed like ‘K. Z.Abd-Elmomiem, A. M. Youssef, and Y. M. Kadah, “Real-Time SpeckleReduction and Coherence Enhancement in Ultrasound Imaging via NonlinearAnisotropic Diffusion”, IEEE transactions on biomedical engineering,vol. 49, NO. 9, Sep. 2002’. However, since the nonlinear anisotropicdiffusion filter needs an operation of solving a partial differentialequation for calculation, there is a problem that it takes a time forthe calculation processing. In addition, although there is an effect ofreducing the speckle to some extent with the single nonlinearanisotropic diffusion filter, there is also a problem that the effect isnot enough.

In both of the two methods described above, an object to be processed isa two-dimensional ultrasonic image and it is premised that the processis performed after scan conversion processing in which a coordinatesystem of the image is converted from a transmitting and receivingsystem to a display system and before display. In this case, a problemoccurs, for example, when only a B-mode image of an image displayed byoverlapping the B-mode image and a color Doppler image each other. Inaddition, a recent display system has high-resolution. For this reason,many pixels should be processed for high resolution after scanconversion processing. This makes it difficult to increase theprocessing speed. In addition, a case in which ultrasonic image data isvolume data is not specifically proposed in the known example.

BRIEF SUMMARY OF THE INVENTION

In view of the above, it is an object of the present invention toprovide an ultrasonic diagnostic apparatus, an ultrasonic imageprocessing apparatus, and an ultrasonic image processing method capableof removing a speckle of two-dimensional or three-dimensional ultrasonicimage data more effectively and at high speed.

According to an aspect of the present invention, there is provided anultrasonic diagnostic apparatus including: a data generating unit thatexecutes transmission and reception of an ultrasonic wave in a B-modewith respect to a predetermined region of a tested body and generatesultrasonic image data; a decomposition unit that hierarchically performsmultiresolution decomposition of the ultrasonic image data and acquireslow-frequency decomposed image data with first to n-th levels (where,‘n’ is a natural number equal to or larger than 2) and high-frequencydecomposed image data with first to n-th levels; a filtering unit thatperforms nonlinear anisotropic diffusion filtering on output data from anext lower layer or the low-frequency decomposed image data in a lowestlayer and generates edge information on a signal, for every layer, fromthe output data from the next lower layer or the low-frequencydecomposed image data in the lowest layer; a high-frequency levelcontrol unit that controls a signal level of the high-frequencydecomposed image data for every layer on the basis of the edgeinformation of each of the layers; and a mixing unit that acquiresultrasonic image data by hierarchically performing multiresolutionmixing of output data of the filtering unit and output data of thehigh-frequency level control unit which are obtained in each of thelayers.

According to another aspect of the present invention, there is providedan ultrasonic image processing apparatus including: a decomposition unitthat hierarchically performs multiresolution decomposition of ultrasonicimage data, which is acquired by executing transmission and reception ofan ultrasonic wave in a B-mode with respect to a predetermined region ofa tested body, and acquires low-frequency decomposed image data withfirst to n-th levels (where, ‘n’ is a natural number equal to or largerthan 2) and high-frequency decomposed image data with first to n-thlevels; a filtering unit that performs nonlinear anisotropic diffusionfiltering on output data from a next lower layer or the low-frequencydecomposed image data in a lowest layer and generates edge informationon a signal, for every layer, from the output data from the next lowerlayer or the low-frequency decomposed image data in the lowest layer; ahigh-frequency level control unit that controls a signal level of thehigh-frequency decomposed image data for every layer on the basis of theedge information of each of the layers; and a mixing unit that acquiresultrasonic image data by hierarchically performing multiresolutionmixing of output data of the filtering unit and output data of thehigh-frequency level control unit which are obtained in each of thelayers.

According to yet another aspect of the present invention, there isprovided an ultrasonic image processing method including: hierarchicallyperforming multiresolution decomposition of ultrasonic image dataacquired by executing transmission and reception of an ultrasonic wavein a B-mode with respect to a predetermined region of a tested body;acquiring low-frequency decomposed image data with first to n-th levels(where, ‘n’ is a natural number equal to or larger than 2) andhigh-frequency decomposed image data with first to n-th levels on thebasis of the multiresolution decomposition; executing nonlinearanisotropic diffusion filtering on output data from a next lower layeror the low-frequency decomposed image data in a lowest layer; generatingedge information on a signal, for every layer, from the output data fromthe next lower layer or the low-frequency decomposed image data in thelowest layer; controlling a signal level of the high-frequencydecomposed image data for every layer on the basis of the edgeinformation of each of the layers; and acquiring ultrasonic image databy hierarchically performing multiresolution mixing of output data of afiltering unit and output data of a high-frequency level control unitwhich are obtained in each of the layers.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING

FIG. 1 is a block diagram illustrating the configuration of anultrasonic diagnostic apparatus 1 according to a first embodiment;

FIG. 2 is a view illustrating the flow of speckle removing processingexecuted in a speckle removing unit 26;

FIG. 3 is a flow chart illustrating the procedure of filteringprocessing of a nonlinear anisotropic diffusion filter 263 c (or 261 c,262 c);

FIG. 4 is a view illustrating the configuration of an ultrasonicdiagnostic apparatus 1 according to a second embodiment;

FIGS. 5A and 5B are views for explaining a speckle removing function inthe second embodiment;

FIG. 6 is a view illustrating the configuration of an ultrasonicdiagnostic apparatus 1 according to a third embodiment; and

FIG. 7 is a view illustrating an example in which a plurality ofthree-dimensional images (a volume rendering image 14 a, a first multiplanar reconstruction image 14 b, and a second multi planarreconstruction image 14 c) are displayed simultaneously on a monitor.

DETAILED DESCRIPTION OF THE INVENTION

Hereinafter, first to third embodiments of the present invention will bedescribed with reference to the accompanying drawings. Moreover, in thefollowing description, components having approximately the same functionand configuration are denoted by the same reference numeral, and arepeated explanation will only be made as needed.

First Embodiment

An embodiment of the present invention will now be described withreference to the accompanying drawings. Moreover, in the followingdescription, components having approximately the same function andconfiguration are denoted by the same reference numeral, and a repeatedexplanation will only be made as needed.

FIG. 1 is a block diagram illustrating the configuration of anultrasonic diagnostic apparatus 1 according to the present embodiment.As shown in the drawing, an ultrasonic diagnostic apparatus 1 includesan ultrasonic probe 12, an input device 13, a monitor 14, an ultrasonicwave transmission unit 21, an ultrasonic wave receiving unit 22, aB-mode processing unit 23, a Doppler processing unit 24, a scanconverter 25, a speckle removing processing unit 26, a control processor(CPU) 28, an internal storage unit 29, and an interface unit 30.Hereinafter, functions of the constituent components will be described.

The ultrasonic probe 12 generates an ultrasonic wave on the basis of adriving signal from the ultrasonic wave transmission unit 21 and has aplurality of piezoelectric vibrators that convert a reflected wave froma tested body into an electric signal, a matching layer provided in thepiezoelectric vibrators, a packing material that prevents propagation ofan ultrasonic wave rearward from the piezoelectric vibrators, and thelike. When ultrasonic waves are transmitted from the ultrasonic probe 12to a tested body P, the transmitted ultrasonic waves are sequentiallyreflected on a discontinuous surface of acoustic impedances of bodytissues and are then received as an echo signal by the ultrasonic probe12. The amplitude of the echo signal depends on a difference of acousticimpedances on the discontinuous surfaces on which the ultrasonic wavesare reflected. In addition, an echo when transmitted ultrasonic wavesare reflected from a moving blood flow, a heart wall, and the like isfrequency shifted depending on a speed component of a moving body in theultrasonic wave transmission direction by the Doppler effect.

The input device 13 is connected to an apparatus body 11 and has variousswitches, buttons, a track ball 13 s, a mouse 13 c, a keyboard 13 d, andthe like used to perform various kinds of instructions from an operator,an instruction for setting a condition or a region of interest (ROI), aninstruction for setting various image quality conditions, and the likeon the apparatus body 11. For example, when a user operates a stopbutton or a FREEZE button of the input device 13, transmission andreception of an ultrasonic wave are stopped and the ultrasonicdiagnostic apparatus is temporarily stopped.

The monitor 14 displays morphological information or blood flowinformation in a living body on the basis of a video signal from thescan converter 25.

The ultrasonic wave transmission unit 21 has a trigger generatingcircuit, a delay circuit, and a pulse circuit which are not shown. Thepulse circuit repeatedly generates a rate pulse for forming atransmitted ultrasonic wave at a predetermined rate frequency fr Hz(period; 1/fr second). In addition, the delay circuit makes ultrasonicwaves converge in the beam shape for every channel and gives a delaytime, which is required for determining transmission directivity, toeach rate pulse. The trigger generating circuit applies a driving pulseto the probe 12 at the timing based on the rate pulse.

In addition, the ultrasonic wave transmission unit 21 has a function ofchanging a transmission frequency, a transmitted driving voltage, andthe like instantaneously in order to execute a predetermined scansequence according to the instruction of the control processor 28. Inparticular, the change of the transmitted driving voltage is realized bya linear amplifier type signal transmission circuit capable of changingthe value instantaneously or a mechanism which performs switching of aplurality of power supply units.

The ultrasonic wave receiving unit 22 has an amplifying circuit, an A/Dconverter, an adder, and the like which are not shown. The amplifyingcircuit amplifies an echo signal received through the probe 12 for everychannel. The A/D converter gives a delay time, which is required todetermine the receiving directivity, to the amplified echo signal, andthen the adder performs adding processing. By this addition, a reflectedcomponent from a direction according to the receiving directivity ofecho signals is emphasized and overall beams in ultrasonic transmissionand reception are formed by the receiving directivity and thetransmission directivity.

The B-mode processing unit 23 receives an echo signal from theultrasonic wave receiving unit 22, performs logarithmic amplificationand envelope detection processing, and generates data in which thesignal strength is expressed as brightness. This data is transmitted tothe scan converter 25 and is displayed on the monitor 14 as a B-modeimage which expresses the strength of a reflected wave with thebrightness.

The Doppler processing unit 24 makes a frequency analysis of speedinformation from the echo signal received from the ultrasonic wavereceiving unit 22, extracts a blood flow or a tissue and a contrast echocomponent due to the Doppler effect, and calculates blood flowinformation, such as an average speed, diffusion, and power, withrespect to multiple points. The acquired blood flow information istransmitted to the scan converter 25 to be color-displayed on themonitor 14 as an average speed image, a diffusion image, a power image,and a combination image thereof.

The scan converter 25 mixes a scanning line signal row of ultrasonicscan with character information, scale, and the like of variousparameters of data received from the B-mode processing unit 23, theDoppler processing unit 24, and the speckle removing processing unit 26,converts the result into a scanning line signal row in a typical videoformat represented by a television, and generates an ultrasonicdiagnostic image as a display image. The scan converter 25 has a storagememory in which image data is stored, for example, so that an operatorcan call an image recorded in a test after diagnosis. In addition, databefore being input to the scan converter 25 is a group of amplitudevalues or brightness values for every spatial position and is called‘raw data’.

The speckle removing processing unit 26 executes processing according toa speckle removing function, which will be described later, on the basisof the control from the control processor 28 using the raw data beforescan conversion.

The control processor 28 has a function as an information processingdevice (computer), and is a control unit that controls an operation ofthe ultrasonic diagnostic apparatus body. The control processor 28 readsfrom the internal storage unit 29 a control program for executing imagegeneration, image display, and the like, loads the control program ontothe memory that the control processor 28 has, and executes calculation,control, and the like on various kinds of processing.

The internal storage unit 29 stores a control program for realizingimage generation and display processing and scan sequence to bedescribed later, diagnostic information (for example, a patient ID anddoctor's opinion), a diagnostic protocol, transmission and receptionconditions, and a program for realizing the speckle removing function, abody mark generating program, and other data groups. Moreover, theinternal storage unit 29 may also be used to store an image in the imagememory 26 as needed. The data in the internal storage unit 29 may alsobe transmitted to an external peripheral device through the interfacecircuit 30.

The interface unit 30 is an interface related to the input device 13, anetwork, and a new external storage device (not shown). Data or ananalysis result of an ultrasonic image obtained by the apparatus may betransmitted to other apparatuses through the network by the interfaceunit 30.

(Speckle Removing Function)

Next, a speckle removing function that the ultrasonic diagnosticapparatus 1 has will be described.

This function is hierarchically performing multiresolution decompositionof image data (raw data) before scan conversion processing, acquiringlow-frequency decomposed image data with first to n-th levels (where,‘n’ is a natural number equal to or larger than 2) and high-frequencydecomposed image data with first to n-th levels, performing nonlinearanisotropic diffusion filtering on output data from a next lower layeror the low-frequency decomposed image data in a lowest layer, andperforming filtering for generating edge information on a signal, forevery layer, from the output data from the next lower layer or thelow-frequency decomposed image data in the lowest layer. In addition, bycontrolling a signal level of the high-frequency decomposed image datafor every layer on the basis of the edge information on each layer andhierarchically performing multiresolution mixing of the output data ofthe nonlinear anisotropic diffusion filter and the output data of thehigh-frequency level control, which are obtained in each layer, speckleremoval is performed by the synergetic effect of the multiresolutiondecomposition and the nonlinear anisotropic diffusion filtering.Furthermore, in the present embodiment, a case in which the number n oflevels of multiresolution decomposition is 3 is exemplified for aspecific explanation. However, the number n is not limited to the aboveexample and may be any value as long as the number n is a natural numberequal to or larger than 2, for example.

FIG. 2 is a view illustrating the flow of processing (speckle removingprocessing) according to the speckle removing function, which isexecuted in the speckle removing processing unit 26. As shown in thedrawing, first, a wavelet transform portion 261 a of level 1 performsmultiresolution decomposition of image data (raw data) input from theB-mode processing unit 23. In addition, the ‘wavelet transform’ hereinmeans discrete wavelet transform. In addition, the wavelet transform isonly an illustration for multiresolution decomposition, and thetechnical spirit of the present invention is not limited to the abovemethod. For example, the multiresolution decomposition may also berealized by other methods, such as the Laplacian pyramid method. As aresult of the multiresolution decomposition, image data afterdecomposition is decomposed into a low-frequency image LL, a horizontaland high-frequency image LH, a vertical and high-frequency image HL, anda diagonal and high-frequency image HH, of which horizontal and verticallengths are half of those before the decomposition. Among the decomposedimage data, the low-frequency image LL is output to a wavelet transformportion 262 a of level 2 and the horizontal and high-frequency image LH,the vertical and high-frequency image HL, and the diagonal andhigh-frequency image HH are output to a high-frequency level controlportion 261 b.

In addition, the wavelet transform portion 262 a of level 2 acquires thelow-frequency image LL, the horizontal and high-frequency image LH, thevertical and high-frequency image HL, and the diagonal andhigh-frequency image HH by performing multiresolution decomposition ofthe low-frequency image LL input from the wavelet transform portion 261a of level 1, and outputs the low-frequency image LL to a wavelettransform portion 263 a of level 2 and outputs the horizontal andhigh-frequency image LH, the vertical and high-frequency image HL, andthe diagonal and high-frequency image HH to a high-frequency levelcontrol portion 262 b.

In addition, the wavelet transform portion 263 a of level 2 acquires thelow-frequency image LL, the horizontal and high-frequency image LH, thevertical and high-frequency image HL, and the diagonal andhigh-frequency image HH by performing multiresolution decomposition ofthe low-frequency image LL input from the wavelet transform portion 262a of level 2, and outputs the low-frequency image LL to a nonlinearanisotropic diffusion filter 263 c of level 3 and outputs the horizontaland high-frequency image LH, the vertical and high-frequency image HL,and the diagonal and high-frequency image HH to a high-frequency levelcontrol portion 263 b.

Then, a nonlinear anisotropic diffusion filter 263 c of level 3 performsfiltering of the low-frequency image LL and outputs the low-frequencyimage LL after the filtering to an inverse wavelet transform portion 263d. In addition, a nonlinear anisotropic diffusion filter 263 c of level3 generates edge information based on the low-frequency image LL andoutputs the edge information to the inverse wavelet transform portion263 b.

Here, a nonlinear anisotropic diffusion filter will be described. Thenonlinear anisotropic diffusion filter is expressed by the followingpartial differential equation (1).

$\begin{matrix}{\frac{\partial I}{\partial T} = {{div}\left\lbrack {D{\nabla I}} \right\rbrack}} & \left\lbrack {{Expression}\mspace{14mu} 1} \right\rbrack\end{matrix}$

‘I’ indicates a pixel level of an image to be processed, ‘∇I’ indicatesthe gradient vector, and ‘t’ indicates a time taken for processing. ‘D’indicates diffusion tensor and may be expressed by the followingexpression (2).

$\begin{matrix}{D = {\begin{pmatrix}d_{11} & d_{12} \\d_{12} & d_{22}\end{pmatrix} = {{R\begin{pmatrix}\lambda_{1} & 0 \\0 & \lambda_{2}\end{pmatrix}}R^{T}}}} & \left\lbrack {{Expression}\mspace{14mu} 2} \right\rbrack\end{matrix}$

‘R’ indicates a rotation matrix, and the diffusion tensor D indicates anoperation of applying coefficients λ₁ and λ₂ to the gradient vector ofeach pixel in a specific direction and a direction perpendicular to thespecific direction. The direction is a direction of the edge of adetected image, and the coefficient depends on the size of the edge.

In order to detect the size and direction of the edge, it is general toacquire the structure tensor of the image and to calculate aneigenvalue. The eigenvalue is related with the size of the edge, and theeigenvector indicates the direction of the edge. The structure tensor isdefined as the following expression (3).

$\begin{matrix}\begin{matrix}{S = {G_{\rho}*\begin{pmatrix}I_{x}^{2} & {I_{x}I_{y}} \\{I_{x}I_{y}} & I_{y}^{2}\end{pmatrix}}} \\{= \begin{pmatrix}{G_{\rho}*I_{x}^{2}} & {G_{\rho}*\left( {I_{x}I_{y}} \right)} \\{G_{\rho}*\left( {I_{x}I_{y}} \right)} & {G_{\rho}*I_{y}^{2}}\end{pmatrix}} \\{= \begin{pmatrix}s_{11} & s_{12} \\s_{12} & s_{22}\end{pmatrix}}\end{matrix} & \left\lbrack {{Expression}\mspace{14mu} 3} \right\rbrack\end{matrix}$

Here, ‘I_(x)’ and ‘I_(y)’ indicate spatial differentiation of the imageI to be processed in x (horizontal) and y (vertical) directions thereof,‘Gp’ indicates a two-dimensional Gaussian function, and an operator ‘*’indicates convolution. Calculation of the size and direction of an edgemay not be necessarily performed according to the above method. Insteadof calculating I_(x) and I_(y) as a first step of processing, a sobelfilter or a high-frequency component of multiresolution decompositionmay also be applied.

Although a method of calculating the coefficients λ₁ and λ₂ changes withcharacteristics of an ultrasonic image in each diagnostic field, it isuseful to prepare a general expression so that the coefficients can beadjusted by some parameters.

In addition, calculation of the filter itself is performed in a numericanalysis method of a partial differential equation. That is, from apixel level of a pixel at a predetermined point and pixel levels of, forexample, nine pixels around the pixel and each element value ofdiffusion tensor at time t, a new pixel level at the point is calculatedat time t+Δt. Then, the same calculation is repeated once to severaltimes with t+Δt as new t.

FIG. 3 is a flow chart illustrating the procedure of filteringprocessing of the nonlinear anisotropic diffusion filter 263 c (or 261 cor 262 c). As shown in the drawing, the nonlinear anisotropic diffusionfilter 263 c differentiates the input low-frequency image LL in the xand y directions (step S1) and calculates the structure tensor s₁₁, s₁₂,and s₂₂ (step S2). In addition, calculation of the Gaussian filter isalso included in the calculation of step S2.

Then, the nonlinear anisotropic diffusion filter 263 c calculates thesize of the edge from each element of the structure tensor (step S3).This calculation result is used for partial differential equationcalculation in a subsequent stage and processing in the high-frequencylevel control portion 263 b (or 262 b or 261 b).

Then, the nonlinear anisotropic diffusion filter 263 c calculates eachcoefficient used in the numerical analysis of the partial differentialequation of the nonlinear anisotropic diffusion filter on the basis ofeach element of the structure tensor (step S4). In addition, in thisstep, calculation of the structure tensor is also included, and the sizeof the edge is also used in the calculation for efficient processing.

Then, the nonlinear anisotropic diffusion filter 263 c executesnumeric-analysis calculation of the partial differential equation onceor several times repeatedly (step S5). The result obtained by thecalculation is output to the inverse wavelet transform portion 263 d (or261 d or 262 d).

Then, as shown in FIG. 2, the high-frequency level control portion 263 bof level 3 is input with the horizontal and high-frequency image LH, thevertical and high-frequency image HL, the diagonal and high-frequencyimage HH, and edge information on these three components and controls ahigh-frequency level according to the images and the edge information.In addition, in the present embodiment, the edge information is the sizeof an edge standardized on the basis of the eigenvalue of the structuretensor, a product of the size and each high-frequency image is taken forevery pixel, and a control coefficient of each high-frequency image isapplied to the result. As another example, there is a method of settinga threshold value for the size of an edge, determining the size of anedge equal to or larger than the threshold value as an edge, andapplying a control coefficient of each high-frequency image to a regionother than the edge. Three high-frequency images processed as describedabove are input to the inverse wavelet transform portion 263 d.

The inverse wavelet transform portion 263 d forms one composite imagefrom the low-frequency image LL output from the nonlinear anisotropicdiffusion filter 263 c and the horizontal and high-frequency image LH,the vertical and high-frequency image HL, and the diagonal andhigh-frequency image HH output from the high-frequency level controlportion 263 b. The horizontal and vertical lengths of the compositeimage are twice those of an input image.

The composite image output from the inverse wavelet transform portion263 d of level 3 is input to the nonlinear anisotropic diffusion filter262 c of level 2, is subjected to the same filtering processing as thelevel 3, and is then transmitted to a low-frequency image input of theinverse wavelet transform portion 262 d. On the other hand, thehorizontal and high-frequency image LH, the vertical and high-frequencyimage HL, and the diagonal and high-frequency image HH output from thewavelet transform portion 262 a are subjected to the same high-frequencylevel control as the level 3 in the high-frequency level control portion262 b and are transmitted to a high-frequency image input of the inversewavelet transform portion 262 d. The inverse wavelet transform portion262 d forms a composite image data from one low-frequency image andthree high-frequency images in the same manner as the level 3.

In addition, the composite image output from the inverse wavelettransform portion 262 d of level 2 is input to the nonlinear anisotropicdiffusion filter 261 c of level 1, is subjected to the same filteringprocessing as the levels 2 and 3, and is then transmitted to alow-frequency image input of the inverse wavelet transform portion 261d. On the other hand, the horizontal and high-frequency image LH, thevertical and high-frequency image HL, and the diagonal andhigh-frequency image HH output from the wavelet transform portion 261 aare subjected to the same high-frequency level control as the levels 2and 3 in the high-frequency level control portion 261 b and aretransmitted to a high-frequency image input of the inverse wavelettransform portion 261 d. The inverse wavelet transform portion 261 dforms a composite image from one low-frequency image and threehigh-frequency images in the same manner as the levels 2 and 3.

The composite image data formed by the above-described processing istransmitted from the speckle removing processing unit 26 to the scanconverter 25. The scan converter 25 mixes the composite image data withcharacter information, scale, and the like of various parameters,converts the result into a scanning line signal row in a normal videoformat represented as a television, and generates an ultrasonicdiagnostic image as a display image. The generated ultrasonic image isexpressed in a predetermined form on the monitor 14.

(Effects)

According to the configuration described above, the following effectscan be obtained. According to the ultrasonic diagnostic apparatus,multiresolution decomposition of image data (raw data) before scanconversion processing is hierarchically performed, low-frequencydecomposed image data with first to n-th levels (where, ‘n’ is a naturalnumber equal to or larger than 2) and high-frequency decomposed imagedata with first to n-th levels are acquired, nonlinear anisotropicdiffusion filtering is performed on output data from a next lower layeror the low-frequency decomposed image data in the lowest layer, andfiltering for generating edge information on a signal for every layer isperformed from the output data from the next lower layer or thelow-frequency decomposed image data in the lowest layer. In addition, bycontrolling a signal level of the high-frequency decomposed image datafor every layer on the basis of the edge information on each layer andhierarchically performing multiresolution mixing of the output data ofthe nonlinear anisotropic diffusion filter and the output data of thehigh-frequency level control, which are obtained in each layer, speckleremoval is performed by the synergetic effect of the multiresolutiondecomposition and the nonlinear anisotropic diffusion filtering.Therefore, compared with a case in which only a filter is applied,speckle removing processing in which the speckle is fine and aninterface of tissues is clearer can be realized. As a result, ahigh-quality diagnostic image can be provided, which can contribute toimproving the quality of image diagnosis.

In addition, according to the ultrasonic diagnostic apparatus, thenonlinear anisotropic diffusion filter is applied after reducing animage by multiresolution decomposition. Accordingly, compared with acase where a nonlinear anisotropic diffusion filter is applied directlyto an original image, the processing area (amount of data to beprocessed) can be reduced. As a result, high-speed processing can berealized compared with a nonlinear anisotropic diffusion filter whichrequires a time for calculation.

Furthermore, according to the ultrasonic diagnostic apparatus, sinceonly a B-mode image is processed in the speckle removing processing, theprocessing does not affect a color Doppler image even if the colorDoppler image overlaps the B-mode image. As a result, high-qualityspeckle removal can be realized without restricting the degree offreedom in image processing or image display and without affecting theprocessing speed even if the resolution of a display system isincreased.

Second Embodiment

In the first embodiment, an example in which speckle removing processingis executed on two-dimensional image data (raw data) has beenillustrated. On the other hand, in the present embodiment, a case inwhich an ultrasonic diagnostic apparatus 1 executes speckle removingprocessing on three-dimensional volume data (raw data) will bedescribed.

FIG. 4 is a view illustrating the configuration of the ultrasonicdiagnostic apparatus 1 according to the present embodiment. Theconfiguration shown in FIG. 4 is different from that shown in FIG. 1 inthat a volume data generating unit 31 is further provided and a speckleremoving processing unit 26 performs speckle removing processing onvolume data from the volume data generating unit 31.

The volume data generating unit 31 generates B-mode volume data usingB-mode image data received from a B-mode processing unit 23. Inaddition, the volume data generating unit 31 generates Doppler-modeimage volume data using Doppler-mode data received from a Dopplerprocessing unit 24.

A three-dimensional image processing unit 32 performs predeterminedimage processing, such as volume rendering, multi planar reconstruction(MPR), and maximum intensity projection (MIP), on the volume datareceived from the volume data generating unit 31 or the B-mode volumedata which is received from the speckle removing processing unit 26 andhas been subjected to speckle removing processing.

FIGS. 5A and 5B are views for explaining a speckle removing function inthe present embodiment. As shown in FIGS. 5A and 5B, among crosssections volume data, two faces which cross a central axis of an objectregion (ultrasonic scan region) of ultrasonic scan executed by using theultrasonic probe 12 and which are perpendicular to each other aredefined as A face and B face, and a face perpendicular to the centralaxis and the A and B faces is defined as a C face.

The B-mode volume data received from the volume data generating unit 31may be assumed to be a group (that is, a group of two-dimensional imagedata parallel to the A face) of ‘m’ planes A0, A1, . . . Am−1 parallelto the A face. The speckle removing processing unit 26 executes speckleremoving processing on the B-mode volume data by performing the speckleremoving processing described in the first embodiment on alltwo-dimensional image data parallel to the A face.

The three-dimensional image processing unit 32 receives the B-modevolume data subjected to the speckle removing processing from thespeckle removing processing unit 26 and the Doppler volume data from thevolume data generating unit 31 and executes image processing, such asvolume rendering, on the basis of the B-mode volume data and the Dopplervolume data. Three-dimensional image data generated by the imageprocessing is converted into a scanning line signal row in a normalvideo format by the scan converter 25 and is displayed in apredetermined form on the monitor 14.

In the ultrasonic diagnostic apparatus according to the presentembodiment, the speckle removing processing can be executed on theentire B-mode volume data by performing the speckle removing processingon all of the two-dimensional image data that forms the B-mode volumedata. As a result, an ultrasonic image from which speckle is removed canbe acquired not only on the A face but about the B and C faces.Particularly on the C face which is required to be smooth, the speckleis fine and an interface of tissues becomes clearer. Accordingly,effective speckle removal can be realized in the entirethree-dimensional space.

Third Embodiment

As described above, in the second embodiment, an example in which thepresent invention is applied to the three-dimensional volume data beforethree-dimensional image processing has been illustrated. However, in athird embodiment, an example in which the present invention is appliedto a three-dimensional display after three-dimensional image processingwill now be illustrated.

In the second embodiment, an example in which speckle removingprocessing is executed on B-mode volume data before three-dimensionalimage processing has been illustrated. On the other hand, in the presentembodiment, a case in which an ultrasonic diagnostic apparatus 1executes speckle removing processing on image data afterthree-dimensional image processing will be described.

FIG. 6 is a view illustrating the configuration of the ultrasonicdiagnostic apparatus 1 according to the present embodiment. Theconfiguration shown in FIG. 6 is different from that shown in FIG. 4 inthat a volume data generating unit 31 is further provided and a speckleremoving processing unit 26 performs speckle removing processing onimage data from a three-dimensional image processing unit 32.

FIG. 7 is a view illustrating an example in which a plurality ofthree-dimensional images (a volume rendering image 14 a, a first multiplanar reconstruction image 14 b, and a second multi planarreconstruction image 14 c) are displayed simultaneously on the monitor14.

The speckle removing processing unit 26 executes, for example, thespeckle removing processing, which was described in the firstembodiment, on three-dimensional image data received from thethree-dimensional image processing unit 32. In this case, for example,when a display form shown in FIG. 7 is adopted, the speckle removingprocessing may be performed on at least one of the volume renderingimage 14 a, the first multi planar reconstruction image 14 b, and thesecond multi planar reconstruction image 14 c. In addition, it isneedless to say that the three-dimensional image data received from thethree-dimensional image processing unit 32 be not limited to examples ofthe volume rendering image 14 a, the first multi planar reconstructionimage 14 b, and the second multi planar reconstruction image 14 c. Forexample, the speckle removing processing may also be executed onthree-dimensional image data obtained by other rendering andreconstruction processing, such as surface rendering and maximumintensity projection.

In addition, the present invention is not limited to the embodimentsdescribed above but may be embodied in practice by modifying constituentcomponents without departing from the scope and spirit of the presentinvention. For example, specific modifications include the followingexamples.

(1) Each of the functions in the present embodiment may be realized byinstalling a program, which is used to execute corresponding processing,in a computer, such as a workstation, and then loading the program ontoa memory.

In this case, a program capable of causing a computer to execute acorresponding technique may be distributed in a state where the programis stored in a recording medium, such as a magnetic disk (for example, afloppy (registered trademark) disk or a hard disk), an optical disk (forexample, a CD-ROM or a DVD), and a semiconductor memory.

(2) In the second embodiment described above, a surface crossing thecentral axis of an ultrasonic scan region is set as a cross section onwhich the speckle removing processing is performed. However, the speckleremoving processing may be performed on an arbitrary cross section of athree-dimensional space without being limited to the example.

In addition, various kinds of inventions may be realized by propercombination of the plurality of constituent components disclosed in theembodiments described above. For example, some constituent componentsmay be eliminated from all components shown in the above embodiments.Moreover, constituent components in different embodiments may beappropriately combined.

1. An ultrasonic diagnostic apparatus comprising: a data generating unitthat executes transmission and reception of an ultrasonic wave in aB-mode with respect to a predetermined region of a tested body andgenerates ultrasonic image data; a decomposition unit thathierarchically performs multiresolution decomposition of the ultrasonicimage data and acquires low-frequency decomposed image data with firstto n-th levels (where, ‘n’ is a natural number equal to or larger than2) and high-frequency decomposed image data with first to n-th levels; afiltering unit that performs nonlinear anisotropic diffusion filteringon output data from a next lower layer or the low-frequency decomposedimage data in a lowest layer and generates edge information on a signal,for every layer, from the output data from the next lower layer or thelow-frequency decomposed image data in the lowest layer; ahigh-frequency level control unit that controls a signal level of thehigh-frequency decomposed image data for every layer on the basis of theedge information of each of the layers; and a mixing unit that acquiresultrasonic image data by hierarchically performing multiresolutionmixing of output data of the filtering unit and output data of thehigh-frequency level control unit which are obtained in each of thelayers.
 2. The ultrasonic diagnostic apparatus according to claim 1,wherein the multiresolution decomposition is wavelet transform, and themultiresolution mixing is inverse wavelet transform.
 3. The ultrasonicdiagnostic apparatus according to claim 1, wherein each of themultiresolution decomposition and the multiresolution mixing is aLaplacian pyramid method.
 4. The ultrasonic diagnostic apparatusaccording to claim 1, wherein the ultrasonic image data is raw databefore scan conversion processing.
 5. The ultrasonic diagnosticapparatus according to claim 1, wherein the ultrasonic image data isvolume data, and the decomposition unit executes the multiresolutiondecomposition on each of a plurality of two-dimensional ultrasonic imagedata that forms the volume data.
 6. The ultrasonic diagnostic apparatusaccording to claim 1, wherein the ultrasonic image data is volume data,and the decomposition unit executes the multiresolution decomposition oneach of a plurality of three-dimensional ultrasonic image data generatedby using the volume data.
 7. An ultrasonic image processing apparatuscomprising: a decomposition unit that hierarchically performsmultiresolution decomposition of ultrasonic image data, which isacquired by executing transmission and reception of an ultrasonic wavein a B-mode with respect to a predetermined region of a tested body, andacquires low-frequency decomposed image data with first to n-th levels(where, ‘n’ is a natural number equal to or larger than 2) andhigh-frequency decomposed image data with first to n-th levels; afiltering unit that performs nonlinear anisotropic diffusion filteringon output data from a next lower layer or the low-frequency decomposedimage data in a lowest layer and generates edge information on a signal,for every layer, from the output data from the next lower layer or thelow-frequency decomposed image data in the lowest layer; ahigh-frequency level control unit that controls a signal level of thehigh-frequency decomposed image data for every layer on the basis of theedge information of each of the layers; and a mixing unit that acquiresultrasonic image data by hierarchically performing multiresolutionmixing of output data of the filtering unit and output data of thehigh-frequency level control unit which are obtained in each of thelayers.
 8. The ultrasonic image processing apparatus according to claim7, wherein the multiresolution decomposition is wavelet transform, andthe multiresolution mixing is inverse wavelet transform.
 9. Theultrasonic image processing apparatus according to claim 7, wherein eachof the multiresolution decomposition and the multiresolution mixing is aLaplacian pyramid method.
 10. The ultrasonic image processing apparatusaccording to claim 7, wherein the ultrasonic image data is raw databefore scan conversion processing.
 11. The ultrasonic image processingapparatus according to claim 7, wherein the ultrasonic image data isvolume data, and the decomposition unit executes the multiresolutiondecomposition on each of a plurality of two-dimensional ultrasonic imagedata that forms the volume data.
 12. The ultrasonic image processingapparatus according to claim 7, wherein the ultrasonic image data isvolume data, and the decomposition unit executes the multiresolutiondecomposition on each of a plurality of three-dimensional ultrasonicimage data generated by using the volume data.
 13. An ultrasonic imageprocessing method comprising: hierarchically performing multiresolutiondecomposition of ultrasonic image data acquired by executingtransmission and reception of an ultrasonic wave in a B-mode withrespect to a predetermined region of a tested body; acquiringlow-frequency decomposed image data with first to n-th levels (where,‘n’ is a natural number equal to or larger than 2) and high-frequencydecomposed image data with first to n-th levels on the basis of themultiresolution decomposition; executing nonlinear anisotropic diffusionfiltering on output data from a next lower layer or the low-frequencydecomposed image data in a lowest layer; generating edge information ona signal, for every layer, from the output data from the next lowerlayer or the low-frequency decomposed image data in the lowest layer;controlling a signal level of the high-frequency decomposed image datafor every layer on the basis of the edge information of each of thelayers; and acquiring ultrasonic image data by hierarchically performingmultiresolution mixing of output data of a filtering unit and outputdata of a high-frequency level control unit which are obtained in eachof the layers.
 14. The ultrasonic image processing method according toclaim 13, wherein the multiresolution decomposition is wavelettransform, and the multiresolution mixing is inverse wavelet transform.15. The ultrasonic image processing method according to claim 13,wherein each of the multiresolution decomposition and themultiresolution mixing is a Laplacian pyramid method.
 16. The ultrasonicimage processing method according to claim 13, wherein the ultrasonicimage data is raw data before scan conversion processing.
 17. Theultrasonic image processing method according to claim 13, wherein theultrasonic image data is volume data, and the decomposition unitexecutes the multiresolution decomposition on each of a plurality oftwo-dimensional ultrasonic image data that forms the volume data. 18.The ultrasonic image processing method according to claim 13, whereinthe ultrasonic image data is volume data, and the decomposition unitexecutes the multiresolution decomposition on each of a plurality ofthree-dimensional ultrasonic image data generated by using the volumedata.